Table REF  presents the quantitative results achieved by the methods on the DTU evaluation dataset. We calculated the three standard error metrics [1]} given on the official site of DTU: accuracy, completeness, and overall error. Following the same setups with most of the baselines, we trained our model on the DTU training set and then used this model for evaluation. Moreover, we also provided the results which produced by training on both DTU and BlendedMVS simultaneously. In both cases, our method exhibited the best performance in terms of completeness and overall error. We also achieved good results for both accuracy and completeness, while the other methods often produced a large trade-off between these two metrics. For example, Gipuma had a high accuracy at \(0.283\)  but it produced very low completeness, \(0.873\) . In contrast, PatchMatchNet was with a high completeness \((0.277)\)  and very low accuracy \((0.427)\) . Moreover, our method outperformed all similar methods using the cascade structure. This indicates that our feature representation and cost aggregation guided by normal curvature can remarkably improve MVS depth prediction remarkably.
